{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import re\n",
    "from itertools import combinations\n",
    "from pathlib import Path\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scanpy as sc\n",
    "from scipy.stats import spearmanr\n",
    "from scipy.stats import t, norm\n",
    "from tqdm import tqdm\n",
    "import argparse\n",
    "from scipy.stats import rankdata\n",
    "from collections import namedtuple\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from scipy import stats\n",
    "%matplotlib inline\n",
    "\n",
    "\n",
    "def get_time(x):\n",
    "    if x == 'UT':\n",
    "        return x\n",
    "    else:\n",
    "        pattern = re.compile(r'\\d+h')\n",
    "        return re.findall(pattern, x)[0]\n",
    "\n",
    "\n",
    "class DATASET:\n",
    "    def __init__(self, datasetname):\n",
    "        self.name = datasetname\n",
    "        self.path_prefix = Path(\"./seurat_objects\")\n",
    "        self.information = self.get_information()\n",
    "    def get_information(self):\n",
    "        if self.name == 'onemillionv2':\n",
    "            self.path = '1M_v2_mediumQC_ctd_rnanormed_demuxids_20201029.sct.h5ad'\n",
    "            self.individual_id_col = 'assignment'\n",
    "            self.timepoint_id_col = 'time'\n",
    "            self.celltype_id = 'cell_type_lowerres'\n",
    "            self.chosen_condition = {'UT': 'UT',\n",
    "                                     'stimulated': '3h'}\n",
    "        elif self.name == 'onemillionv3':\n",
    "            self.path = '1M_v3_mediumQC_ctd_rnanormed_demuxids_20201106.SCT.h5ad'\n",
    "            self.individual_id_col = 'assignment'\n",
    "            self.timepoint_id_col = 'time'\n",
    "            self.celltype_id = 'cell_type_lowerres'\n",
    "            self.chosen_condition = {'UT': 'UT',\n",
    "                                     'stimulated': '3h'}\n",
    "        elif self.name == 'stemiv2':\n",
    "            self.path = 'cardio.integrated.20210301.stemiv2.h5ad'\n",
    "            self.individual_id_col = 'assignment.final'\n",
    "            self.timepoint_id_col = 'timepoint.final'\n",
    "            self.celltype_id = 'cell_type_lowerres'\n",
    "            self.chosen_condition = {'UT': 't8w',\n",
    "                                     'stimulated': 'Baseline'}\n",
    "        elif self.name == 'ng':\n",
    "            self.path = 'pilot3_seurat3_200420_sct_azimuth.h5ad'\n",
    "            self.individual_id_col = 'snumber'\n",
    "            self.celltype_id = 'cell_type_mapped_to_onemillion'\n",
    "        else:\n",
    "            raise IOError(\"Dataset name not understood.\")\n",
    "    def load_dataset(self):\n",
    "        self.get_information()\n",
    "        print(f'Loading dataset {self.name} from {self.path_prefix} {self.path}')\n",
    "        self.data_sc = sc.read_h5ad(self.path_prefix / self.path)\n",
    "        if self.name.startswith('onemillion'):\n",
    "            self.data_sc.obs['time'] = [get_time(item) for item in self.data_sc.obs['timepoint']]\n",
    "        elif self.name == 'ng':\n",
    "            celltype_maping = {'CD4 T': 'CD4T', 'CD8 T': 'CD8T', 'Mono': 'monocyte', 'DC': 'DC', 'NK': 'NK',\n",
    "                               'other T': 'otherT', 'other': 'other', 'B': 'B'}\n",
    "            self.data_sc.obs['cell_type_mapped_to_onemillion'] = [celltype_maping.get(name) for name in\n",
    "                                                          self.data_sc.obs['predicted.celltype.l1']]\n",
    "\n",
    "def corr_to_z(coef, num):\n",
    "    t_statistic = coef * np.sqrt((num - 2) / (1 - coef ** 2))\n",
    "    prob = t.cdf(t_statistic, num - 2)\n",
    "    z_score = norm.ppf(prob)\n",
    "    positive_coef_probs = 1 - prob\n",
    "    positive_coef_probs[coef < 0] = 0\n",
    "    negative_coef_probs = prob\n",
    "    negative_coef_probs[coef > 0] = 0\n",
    "    probs = negative_coef_probs + positive_coef_probs\n",
    "    return z_score, probs\n",
    "\n",
    "\n",
    "def get_individual_networks_selected_genepairs(data_df, data_sc, individual_colname, genepair):\n",
    "#     data_df = pd.DataFrame(data=data_sc.X.toarray(),\n",
    "#                            index=data_sc.obs.index,\n",
    "#                            columns=data_sc.var.index)\n",
    "    gene1, gene2 = genepair.split(';')\n",
    "    sorted_genepair = [';'.join(sorted([gene1, gene2]))]\n",
    "    coef_df = pd.DataFrame(index=sorted_genepair)\n",
    "    coef_p_df = pd.DataFrame(index=sorted_genepair)\n",
    "    zscore_df = pd.DataFrame(index=sorted_genepair)\n",
    "    zscore_p_df = pd.DataFrame(index=sorted_genepair)\n",
    "    data_selected_df = data_df[[gene1, gene2]]\n",
    "    print(\n",
    "        f\"Begin calculating networks for {len(data_sc.obs[individual_colname].unique())} individuals and;\\n{genepair}\"\n",
    "    )\n",
    "    for ind_id in tqdm(data_sc.obs[individual_colname].unique()):\n",
    "        cell_num = data_sc.obs[data_sc.obs[individual_colname] == ind_id].shape[0]\n",
    "        if cell_num > 10:\n",
    "            individual_df = data_selected_df.loc[data_sc.obs[individual_colname] == ind_id]\n",
    "            individual_coefs, individual_coef_ps = spearmanr(individual_df.values, axis=0)\n",
    "            if data_selected_df.shape[1] == 2:\n",
    "                individual_coefs_flatten = pd.DataFrame(data = [individual_coefs],\n",
    "                                                        index = sorted_genepair)\n",
    "                individual_coef_ps_flatten = \\\n",
    "                pd.DataFrame(data=[individual_coef_ps],\n",
    "                             index=sorted_genepair)\n",
    "            else:\n",
    "                individual_coefs_flatten = pd.DataFrame(\n",
    "                    data=individual_coefs[np.triu_indices_from(individual_coefs, 1)],\n",
    "                    index=selected_genes_sorted_genepairs).loc[sorted_genepair]\n",
    "                individual_coef_ps_flatten = \\\n",
    "                pd.DataFrame(data=individual_coef_ps[np.triu_indices_from(individual_coefs, 1)],\n",
    "                             index=selected_genes_sorted_genepairs).loc[sorted_genepair]\n",
    "            coef_df[ind_id] = individual_coefs_flatten\n",
    "            coef_p_df[ind_id] = individual_coef_ps_flatten\n",
    "            try:\n",
    "#                 print(individual_coefs_flatten.values, cell_num)\n",
    "                individual_zscores_flatten, individual_zscore_ps_flatten = corr_to_z(\n",
    "                    individual_coefs_flatten.values, \n",
    "                    cell_num\n",
    "                )\n",
    "                zscore_df[ind_id] = individual_zscores_flatten\n",
    "                zscore_p_df[ind_id] = individual_zscore_ps_flatten\n",
    "            except:\n",
    "                continue\n",
    "        else:\n",
    "            print(\"Deleted this individual because of low cell number\", cell_num)\n",
    "    return data_selected_df, zscore_df, zscore_p_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### One million data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load the GT data\n",
    "gt = pd.read_csv('./coeqtl_interpretation/rs1131017_TEM_ratio/rs1131017.vcf',\n",
    "                 skiprows=6, sep='\\t')\n",
    "change_colnames = lambda col:'_'.join(col.split('_')[1:]) if 'LLDeep' in col else col\n",
    "gt = gt.rename({col:change_colnames(col) for col in gt.columns}, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>#CHROM</th>\n",
       "      <th>POS</th>\n",
       "      <th>ID</th>\n",
       "      <th>REF</th>\n",
       "      <th>ALT</th>\n",
       "      <th>QUAL</th>\n",
       "      <th>FILTER</th>\n",
       "      <th>INFO</th>\n",
       "      <th>FORMAT</th>\n",
       "      <th>LLDeep_1191</th>\n",
       "      <th>...</th>\n",
       "      <th>s21</th>\n",
       "      <th>s43</th>\n",
       "      <th>s24</th>\n",
       "      <th>s23</th>\n",
       "      <th>s45</th>\n",
       "      <th>s26</th>\n",
       "      <th>s25</th>\n",
       "      <th>s28</th>\n",
       "      <th>s27</th>\n",
       "      <th>s29</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12</td>\n",
       "      <td>56435929</td>\n",
       "      <td>rs1131017</td>\n",
       "      <td>C</td>\n",
       "      <td>G</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>GT:DS</td>\n",
       "      <td>0/0:0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1/1:2.0</td>\n",
       "      <td>1/1:2.0</td>\n",
       "      <td>1/1:2.0</td>\n",
       "      <td>0/1:1.0</td>\n",
       "      <td>1/1:2.0</td>\n",
       "      <td>0/1:1.0</td>\n",
       "      <td>0/0:0.0</td>\n",
       "      <td>1/1:2.0</td>\n",
       "      <td>0/0:0.06000000000000005</td>\n",
       "      <td>1/1:2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 182 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   #CHROM       POS         ID REF ALT QUAL FILTER INFO FORMAT LLDeep_1191  \\\n",
       "0      12  56435929  rs1131017   C   G    .      .    .  GT:DS     0/0:0.0   \n",
       "\n",
       "   ...      s21      s43      s24      s23      s45      s26      s25  \\\n",
       "0  ...  1/1:2.0  1/1:2.0  1/1:2.0  0/1:1.0  1/1:2.0  0/1:1.0  0/0:0.0   \n",
       "\n",
       "       s28                      s27      s29  \n",
       "0  1/1:2.0  0/0:0.06000000000000005  1/1:2.0  \n",
       "\n",
       "[1 rows x 182 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gt.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CD4T+CD8T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # load onemillion v2 data\n",
    "# dataset = DATASET('onemillionv2')\n",
    "# dataset.load_dataset()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>assignment</th>\n",
       "      <th>predicted.celltype.l1</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">LLDeep_0022</th>\n",
       "      <th>B</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CD4 T</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CD8 T</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DC</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mono</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   0\n",
       "assignment  predicted.celltype.l1   \n",
       "LLDeep_0022 B                      0\n",
       "            CD4 T                  0\n",
       "            CD8 T                  0\n",
       "            DC                     0\n",
       "            Mono                   0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>assignment</th>\n",
       "      <th>predicted.celltype.l2</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">LLDeep_0022</th>\n",
       "      <th>ASDC</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B intermediate</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B memory</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B naive</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CD14 Mono</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   0\n",
       "assignment  predicted.celltype.l2   \n",
       "LLDeep_0022 ASDC                   0\n",
       "            B intermediate         0\n",
       "            B memory               0\n",
       "            B naive                0\n",
       "            CD14 Mono              0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CD4T TEM</th>\n",
       "      <th>CD4T Naive</th>\n",
       "      <th>CD8T TEM</th>\n",
       "      <th>CD8T Naive</th>\n",
       "      <th>CD4T</th>\n",
       "      <th>CD8T</th>\n",
       "      <th>CD4T TCM</th>\n",
       "      <th>CD8T TCM</th>\n",
       "      <th>all_num</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>LLDeep_1370</th>\n",
       "      <td>20</td>\n",
       "      <td>85</td>\n",
       "      <td>74</td>\n",
       "      <td>13</td>\n",
       "      <td>224</td>\n",
       "      <td>89</td>\n",
       "      <td>85</td>\n",
       "      <td>8</td>\n",
       "      <td>827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LLDeep_0434</th>\n",
       "      <td>42</td>\n",
       "      <td>417</td>\n",
       "      <td>95</td>\n",
       "      <td>93</td>\n",
       "      <td>989</td>\n",
       "      <td>209</td>\n",
       "      <td>406</td>\n",
       "      <td>29</td>\n",
       "      <td>1496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LLDeep_1319</th>\n",
       "      <td>110</td>\n",
       "      <td>132</td>\n",
       "      <td>223</td>\n",
       "      <td>4</td>\n",
       "      <td>922</td>\n",
       "      <td>236</td>\n",
       "      <td>546</td>\n",
       "      <td>19</td>\n",
       "      <td>1504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LLDeep_0269</th>\n",
       "      <td>21</td>\n",
       "      <td>58</td>\n",
       "      <td>80</td>\n",
       "      <td>7</td>\n",
       "      <td>211</td>\n",
       "      <td>88</td>\n",
       "      <td>101</td>\n",
       "      <td>8</td>\n",
       "      <td>529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LLDeep_0471</th>\n",
       "      <td>27</td>\n",
       "      <td>242</td>\n",
       "      <td>81</td>\n",
       "      <td>14</td>\n",
       "      <td>570</td>\n",
       "      <td>103</td>\n",
       "      <td>254</td>\n",
       "      <td>10</td>\n",
       "      <td>1241</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             CD4T TEM  CD4T Naive  CD8T TEM  CD8T Naive  CD4T  CD8T  CD4T TCM  \\\n",
       "LLDeep_1370        20          85        74          13   224    89        85   \n",
       "LLDeep_0434        42         417        95          93   989   209       406   \n",
       "LLDeep_1319       110         132       223           4   922   236       546   \n",
       "LLDeep_0269        21          58        80           7   211    88       101   \n",
       "LLDeep_0471        27         242        81          14   570   103       254   \n",
       "\n",
       "             CD8T TCM  all_num  \n",
       "LLDeep_1370         8      827  \n",
       "LLDeep_0434        29     1496  \n",
       "LLDeep_1319        19     1504  \n",
       "LLDeep_0269         8      529  \n",
       "LLDeep_0471        10     1241  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'rs1131017')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "onemillionv2 = dataset.data_sc.obs.copy()\n",
    "onemillionv2_celltype_df = pd.read_csv(\n",
    "    './1M_v2_20201029_azimuth.tsv',\n",
    "    sep='\\t', index_col=0\n",
    ")\n",
    "onemillionv2 = pd.concat([onemillionv2, onemillionv2_celltype_df], axis=1)\n",
    "onemillionv2 = onemillionv2[onemillionv2['timepoint']=='UT']\n",
    "onemillionv2_l1_cellratio_df = onemillionv2.groupby(['assignment', 'predicted.celltype.l1']).size().to_frame()\n",
    "display(onemillionv2_l1_cellratio_df.head())\n",
    "onemillionv2_celltyperatio = onemillionv2.groupby(['assignment', 'predicted.celltype.l2']).size().to_frame()\n",
    "display(onemillionv2_celltyperatio.head())\n",
    "onemillionv2_allcells = onemillionv2['assignment'].value_counts()\n",
    "\n",
    "# caluclate the individual CD4T TEM and NAIVE ratio\n",
    "individual_ratio = pd.DataFrame()\n",
    "for individual in onemillionv2['assignment'].unique():\n",
    "    tem_num = onemillionv2_celltyperatio.loc[individual, \"CD4 TEM\"].values[0]\n",
    "    naive_num = onemillionv2_celltyperatio.loc[individual, \"CD4 Naive\"].values[0]\n",
    "    cd8t_tem_num = onemillionv2_celltyperatio.loc[individual, \"CD8 TEM\"].values[0]\n",
    "    tcm_num = onemillionv2_celltyperatio.loc[individual, \"CD4 TCM\"].values[0]\n",
    "    cd8t_tcm_num = onemillionv2_celltyperatio.loc[individual, \"CD8 TCM\"].values[0]\n",
    "    cd8t_naive_num = onemillionv2_celltyperatio.loc[individual, \"CD8 Naive\"].values[0]\n",
    "    cd4t_num = onemillionv2_l1_cellratio_df.loc[individual, 'CD4 T'].values[0]\n",
    "    cd8t_num = onemillionv2_l1_cellratio_df.loc[individual, 'CD8 T'].values[0]\n",
    "    all_num = onemillionv2_allcells.loc[individual]\n",
    "    individual_ratio[individual] = [tem_num,  naive_num, \n",
    "                                    cd8t_tem_num, cd8t_naive_num,\n",
    "                                    cd4t_num, cd8t_num,\n",
    "                                    tcm_num, cd8t_tcm_num, all_num]\n",
    "\n",
    "individual_ratio_df = individual_ratio.T\n",
    "individual_ratio_df = individual_ratio_df.rename({0: 'CD4T TEM', 1:'CD4T Naive', \n",
    "                                                  2: 'CD8T TEM', 3: 'CD8T Naive',\n",
    "                                                  4: 'CD4T', 5: 'CD8T',\n",
    "                                                  6: 'CD4T TCM', 7: 'CD8T TCM',\n",
    "                                                  8: 'all_num'}, \n",
    "                                                 axis=1)\n",
    "display(individual_ratio_df.head())\n",
    "\n",
    "\n",
    "common_individuals = list(set(individual_ratio_df.index) & set(gt.columns))\n",
    "common_individuals_individual_ratio_df = individual_ratio_df.loc[common_individuals]\n",
    "common_individuals_individual_ratio_df['gt'] = [float(gt[col].values[0].split(':')[1]) for col in \n",
    "                                                common_individuals_individual_ratio_df.index]\n",
    "common_individuals_individual_ratio_df['chemistry'] = 'v2'\n",
    "\n",
    "fig, axes = plt.subplots(1, 3, figsize=(15, 5))\n",
    "ax1, ax2, ax3 = axes\n",
    "cd4ydata = (common_individuals_individual_ratio_df['CD4T TEM']) / common_individuals_individual_ratio_df['CD4T']\n",
    "sns.regplot(x=common_individuals_individual_ratio_df['gt'],\n",
    "            y=cd4ydata, \n",
    "            ax=ax1)\n",
    "r, p = stats.pearsonr(common_individuals_individual_ratio_df['gt'],\n",
    "                       cd4ydata)\n",
    "ax1.set_title('Oelen v2 r={:.2f}, p={:.2g}'.format(r, p))\n",
    "ax1.set_ylabel('CD4 TEM / CD4T')\n",
    "ax1.set_xlabel(\"rs1131017\")\n",
    "\n",
    "cd8tydata = (common_individuals_individual_ratio_df['CD4T Naive']) / common_individuals_individual_ratio_df['CD4T']\n",
    "sns.regplot(x=common_individuals_individual_ratio_df['gt'],\n",
    "            y= cd8tydata, \n",
    "            ax=ax2)\n",
    "r, p = stats.pearsonr(common_individuals_individual_ratio_df['gt'],\n",
    "                       cd8tydata)\n",
    "ax2.set_title('Oelen v2 r={:.2f}, p={:.2g}'.format(r, p))\n",
    "ax2.set_ylabel('CD4 Naive / CD4T')\n",
    "ax2.set_xlabel(\"rs1131017\")\n",
    "\n",
    "cd8tydata = (common_individuals_individual_ratio_df['CD4T Naive']) / common_individuals_individual_ratio_df['CD4T TEM']\n",
    "sns.regplot(x=common_individuals_individual_ratio_df['gt'],\n",
    "            y= cd8tydata, \n",
    "            ax=ax3)\n",
    "r, p = stats.pearsonr(common_individuals_individual_ratio_df['gt'],\n",
    "                       cd8tydata)\n",
    "ax3.set_title('Oelen v2 r={:.2f}, p={:.2g}'.format(r, p))\n",
    "ax3.set_ylabel('CD4 Naive / CD4T TEM')\n",
    "ax3.set_xlabel(\"rs1131017\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'rs1131017')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 3, figsize=(15, 5))\n",
    "ax1, ax2, ax3 = axes\n",
    "cd4ydata = (common_individuals_individual_ratio_df['CD8T TEM']) / common_individuals_individual_ratio_df['CD8T']\n",
    "sns.regplot(x=common_individuals_individual_ratio_df['gt'],\n",
    "            y=cd4ydata, \n",
    "            ax=ax1)\n",
    "r, p = stats.pearsonr(common_individuals_individual_ratio_df['gt'],\n",
    "                       cd4ydata)\n",
    "ax1.set_title('Oelen v2 r={:.2f}, p={:.2g}'.format(r, p))\n",
    "ax1.set_ylabel('CD8 TEM / CD8T')\n",
    "ax1.set_xlabel(\"rs1131017\")\n",
    "\n",
    "cd8tydata = (common_individuals_individual_ratio_df['CD8T Naive']) / common_individuals_individual_ratio_df['CD8T']\n",
    "sns.regplot(x=common_individuals_individual_ratio_df['gt'],\n",
    "            y= cd8tydata, \n",
    "            ax=ax2)\n",
    "r, p = stats.pearsonr(common_individuals_individual_ratio_df['gt'],\n",
    "                       cd8tydata)\n",
    "ax2.set_title('Oelen v2 r={:.2f}, p={:.2g}'.format(r, p))\n",
    "ax2.set_ylabel('CD8 Naive / CD8T')\n",
    "ax2.set_xlabel(\"rs1131017\")\n",
    "\n",
    "cd8tydata = (common_individuals_individual_ratio_df['CD8T Naive']) / common_individuals_individual_ratio_df['CD8T TEM']\n",
    "sns.regplot(x=common_individuals_individual_ratio_df['gt'],\n",
    "            y= cd8tydata, \n",
    "            ax=ax3)\n",
    "r, p = stats.pearsonr(common_individuals_individual_ratio_df['gt'],\n",
    "                       cd8tydata)\n",
    "ax3.set_title('Oelen v2 r={:.2f}, p={:.2g}'.format(r, p))\n",
    "ax3.set_ylabel('CD8 Naive / CD8T TEM')\n",
    "ax3.set_xlabel(\"rs1131017\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'rs1131017')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cd8tydata = (common_individuals_individual_ratio_df['CD8T TEM'] + \\\n",
    "            common_individuals_individual_ratio_df['CD4T TEM']) / (\n",
    "    common_individuals_individual_ratio_df['CD8T Naive'] + \\\n",
    "    common_individuals_individual_ratio_df['CD4T Naive']\n",
    ")\n",
    "fig, ax = plt.subplots()\n",
    "sns.regplot(x=common_individuals_individual_ratio_df['gt'],\n",
    "            y= cd8tydata, \n",
    "            ax=ax)\n",
    "r, p = stats.spearmanr(common_individuals_individual_ratio_df['gt'],\n",
    "                       cd8tydata)\n",
    "ax.set_title('Oelen v2 r={:.2f}, p={:.2g}'.format(r, p))\n",
    "ax.set_ylabel('CD8+CD4 TEM / CD8+CD4 Naive')\n",
    "ax.set_xlabel(\"rs1131017\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Begin calculating networks for 72 individuals and;\n",
      "RPS26;RUNX3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 72/72 [00:00<00:00, 207.95it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SpearmanrResult(correlation=0.29651955126400936, pvalue=0.011433091246178868)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='gt', ylabel='RPS26;RUNX3'>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "onemillionv2_datasc = dataset.data_sc\n",
    "onemillionv2_monocytes_ut = onemillionv2_datasc[(onemillionv2_datasc.obs['cell_type_lowerres']=='CD4T') & \n",
    "                                                (onemillionv2_datasc.obs['time']=='UT')]\n",
    "onemillionv2_monocytes_ut_df = pd.DataFrame(\n",
    "    data=onemillionv2_monocytes_ut.X.toarray(),\n",
    "    columns=onemillionv2_monocytes_ut.var.index,\n",
    "    index=onemillionv2_monocytes_ut.obs.index\n",
    ")\n",
    "data_selected_df, zscore_df, zscore_p_df = \\\n",
    "get_individual_networks_selected_genepairs(onemillionv2_monocytes_ut_df, \n",
    "                                           onemillionv2_monocytes_ut, \n",
    "                                           'assignment', \n",
    "                                           ';'.join(['RPS26', 'RUNX3']))\n",
    "concated_df = pd.concat([zscore_df.T,\n",
    "           gt.T],\n",
    "          axis=1).dropna()\n",
    "concated_df['gt'] = [item.split(':')[0].count('1') for item in concated_df[0]]\n",
    "print(spearmanr(concated_df['RPS26;RUNX3'], concated_df['gt']))\n",
    "sns.regplot(x='gt', y='RPS26;RUNX3', data=concated_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Begin calculating networks for 72 individuals and;\n",
      "RPS26;RUNX3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 72/72 [00:00<00:00, 214.76it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SpearmanrResult(correlation=0.2201691525430256, pvalue=0.06311568667519006)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='gt', ylabel='RPS26;RUNX3'>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "onemillionv2_datasc = dataset.data_sc\n",
    "onemillionv2_monocytes_ut = onemillionv2_datasc[(onemillionv2_datasc.obs['cell_type_lowerres']=='CD8T') & \n",
    "                                                (onemillionv2_datasc.obs['time']=='UT')]\n",
    "onemillionv2_monocytes_ut_df = pd.DataFrame(\n",
    "    data=onemillionv2_monocytes_ut.X.toarray(),\n",
    "    columns=onemillionv2_monocytes_ut.var.index,\n",
    "    index=onemillionv2_monocytes_ut.obs.index\n",
    ")\n",
    "data_selected_df, zscore_df, zscore_p_df = \\\n",
    "get_individual_networks_selected_genepairs(onemillionv2_monocytes_ut_df, \n",
    "                                           onemillionv2_monocytes_ut, \n",
    "                                           'assignment', \n",
    "                                           ';'.join(['RPS26', 'RUNX3']))\n",
    "concated_df = pd.concat([zscore_df.T,\n",
    "           gt.T],\n",
    "          axis=1).dropna()\n",
    "concated_df['gt'] = [item.split(':')[0].count('1') for item in concated_df[0]]\n",
    "print(spearmanr(concated_df['RPS26;RUNX3'], concated_df['gt']))\n",
    "sns.regplot(x='gt', y='RPS26;RUNX3', data=concated_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### onemillion v3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # load onemillion v2 data\n",
    "# datasetv3 = DATASET('onemillionv3')\n",
    "# datasetv3.load_dataset()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CD4T TEM</th>\n",
       "      <th>CD4T Naive</th>\n",
       "      <th>CD8T TEM</th>\n",
       "      <th>CD8T Naive</th>\n",
       "      <th>CD4T</th>\n",
       "      <th>CD8T</th>\n",
       "      <th>CD4T TCM</th>\n",
       "      <th>CD8T TCM</th>\n",
       "      <th>all_num</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>LLDeep_0117</th>\n",
       "      <td>39</td>\n",
       "      <td>248</td>\n",
       "      <td>70</td>\n",
       "      <td>75</td>\n",
       "      <td>625</td>\n",
       "      <td>170</td>\n",
       "      <td>289</td>\n",
       "      <td>26</td>\n",
       "      <td>1599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LLDeep_1300</th>\n",
       "      <td>34</td>\n",
       "      <td>149</td>\n",
       "      <td>73</td>\n",
       "      <td>39</td>\n",
       "      <td>658</td>\n",
       "      <td>119</td>\n",
       "      <td>450</td>\n",
       "      <td>10</td>\n",
       "      <td>1382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LLDeep_0615</th>\n",
       "      <td>68</td>\n",
       "      <td>84</td>\n",
       "      <td>175</td>\n",
       "      <td>23</td>\n",
       "      <td>550</td>\n",
       "      <td>212</td>\n",
       "      <td>347</td>\n",
       "      <td>19</td>\n",
       "      <td>1277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LLDeep_0923</th>\n",
       "      <td>39</td>\n",
       "      <td>80</td>\n",
       "      <td>205</td>\n",
       "      <td>40</td>\n",
       "      <td>316</td>\n",
       "      <td>249</td>\n",
       "      <td>140</td>\n",
       "      <td>6</td>\n",
       "      <td>907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LLDeep_0705</th>\n",
       "      <td>13</td>\n",
       "      <td>114</td>\n",
       "      <td>123</td>\n",
       "      <td>61</td>\n",
       "      <td>322</td>\n",
       "      <td>188</td>\n",
       "      <td>166</td>\n",
       "      <td>3</td>\n",
       "      <td>947</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             CD4T TEM  CD4T Naive  CD8T TEM  CD8T Naive  CD4T  CD8T  CD4T TCM  \\\n",
       "LLDeep_0117        39         248        70          75   625   170       289   \n",
       "LLDeep_1300        34         149        73          39   658   119       450   \n",
       "LLDeep_0615        68          84       175          23   550   212       347   \n",
       "LLDeep_0923        39          80       205          40   316   249       140   \n",
       "LLDeep_0705        13         114       123          61   322   188       166   \n",
       "\n",
       "             CD8T TCM  all_num  \n",
       "LLDeep_0117        26     1599  \n",
       "LLDeep_1300        10     1382  \n",
       "LLDeep_0615        19     1277  \n",
       "LLDeep_0923         6      907  \n",
       "LLDeep_0705         3      947  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'rs1131017')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "onemillionv3 = datasetv3.data_sc.obs.copy()\n",
    "onemillionv3_celltype_df = pd.read_csv(\n",
    "    './1M_v3_20201106_azimuth.tsv',\n",
    "    sep='\\t', index_col=0\n",
    ")\n",
    "onemillionv3 = pd.concat([onemillionv3, onemillionv3_celltype_df], axis=1)\n",
    "onemillionv3 = onemillionv3[onemillionv3['timepoint']=='UT']\n",
    "onemillionv3_l1_cellratio_df = onemillionv3.groupby(['assignment', 'predicted.celltype.l1']).size().to_frame()\n",
    "# display(onemillionv3_l1_cellratio_df.head())\n",
    "onemillionv3_celltyperatio = onemillionv3.groupby(['assignment', 'predicted.celltype.l2']).size().to_frame()\n",
    "# display(onemillionv3_celltyperatio.head())\n",
    "onemillionv3_allcells = onemillionv3['assignment'].value_counts()\n",
    "\n",
    "# caluclate the individual CD4T TEM and NAIVE ratio\n",
    "individual_ratio = pd.DataFrame()\n",
    "for individual in onemillionv3['assignment'].unique():\n",
    "    tem_num = onemillionv3_celltyperatio.loc[individual, \"CD4 TEM\"].values[0]\n",
    "    naive_num = onemillionv3_celltyperatio.loc[individual, \"CD4 Naive\"].values[0]\n",
    "    cd8t_tem_num = onemillionv3_celltyperatio.loc[individual, \"CD8 TEM\"].values[0]\n",
    "    tcm_num = onemillionv3_celltyperatio.loc[individual, \"CD4 TCM\"].values[0]\n",
    "    cd8t_tcm_num = onemillionv3_celltyperatio.loc[individual, \"CD8 TCM\"].values[0]\n",
    "    cd8t_naive_num = onemillionv3_celltyperatio.loc[individual, \"CD8 Naive\"].values[0]\n",
    "    cd4t_num = onemillionv3_l1_cellratio_df.loc[individual, 'CD4 T'].values[0]\n",
    "    cd8t_num = onemillionv3_l1_cellratio_df.loc[individual, 'CD8 T'].values[0]\n",
    "    all_num = onemillionv3_allcells.loc[individual]\n",
    "    individual_ratio[individual] = [tem_num,  naive_num, \n",
    "                                    cd8t_tem_num, cd8t_naive_num,\n",
    "                                    cd4t_num, cd8t_num,\n",
    "                                    tcm_num, cd8t_tcm_num, all_num]\n",
    "\n",
    "individual_ratio_dfv3 = individual_ratio.T\n",
    "individual_ratio_dfv3 = individual_ratio_dfv3.rename({0: 'CD4T TEM', 1:'CD4T Naive', \n",
    "                                                  2: 'CD8T TEM', 3: 'CD8T Naive',\n",
    "                                                  4: 'CD4T', 5: 'CD8T',\n",
    "                                                  6: 'CD4T TCM', 7: 'CD8T TCM',\n",
    "                                                  8: 'all_num'}, \n",
    "                                                 axis=1)\n",
    "display(individual_ratio_dfv3.head())\n",
    "\n",
    "\n",
    "common_individuals = list(set(individual_ratio_dfv3.index) & set(gt.columns))\n",
    "common_individuals_individual_ratio_dfv3 = individual_ratio_dfv3.loc[common_individuals]\n",
    "common_individuals_individual_ratio_dfv3['gt'] = [float(gt[col].values[0].split(':')[1]) for col in \n",
    "                                                  common_individuals_individual_ratio_dfv3.index]\n",
    "common_individuals_individual_ratio_dfv3['chemistry'] = 'v2'\n",
    "\n",
    "fig, axes = plt.subplots(1, 3, figsize=(15, 5))\n",
    "ax1, ax2, ax3 = axes\n",
    "cd4ydata = (common_individuals_individual_ratio_dfv3['CD4T TEM']) / common_individuals_individual_ratio_dfv3['CD4T']\n",
    "sns.regplot(x=common_individuals_individual_ratio_dfv3['gt'],\n",
    "            y=cd4ydata, \n",
    "            ax=ax1)\n",
    "r, p = stats.pearsonr(common_individuals_individual_ratio_dfv3['gt'],\n",
    "                       cd4ydata)\n",
    "ax1.set_title('Oelen v3 r={:.2f}, p={:.2g}'.format(r, p))\n",
    "ax1.set_ylabel('CD4 TEM / CD4T')\n",
    "ax1.set_xlabel(\"rs1131017\")\n",
    "\n",
    "cd8tydata = (common_individuals_individual_ratio_dfv3['CD4T Naive']) / common_individuals_individual_ratio_dfv3['CD4T']\n",
    "sns.regplot(x=common_individuals_individual_ratio_dfv3['gt'],\n",
    "            y= cd8tydata, \n",
    "            ax=ax2)\n",
    "r, p = stats.pearsonr(common_individuals_individual_ratio_dfv3['gt'],\n",
    "                       cd8tydata)\n",
    "ax2.set_title('Oelen v3 r={:.2f}, p={:.2g}'.format(r, p))\n",
    "ax2.set_ylabel('CD4 Naive / CD4T')\n",
    "ax2.set_xlabel(\"rs1131017\")\n",
    "\n",
    "cd8tydata = (common_individuals_individual_ratio_dfv3['CD4T Naive']) / common_individuals_individual_ratio_dfv3['CD4T TEM']\n",
    "sns.regplot(x=common_individuals_individual_ratio_dfv3['gt'],\n",
    "            y= cd8tydata, \n",
    "            ax=ax3)\n",
    "r, p = stats.pearsonr(common_individuals_individual_ratio_dfv3['gt'],\n",
    "                       cd8tydata)\n",
    "ax3.set_title('Oelen v3 r={:.2f}, p={:.2g}'.format(r, p))\n",
    "ax3.set_ylabel('CD4 Naive / CD4T TEM')\n",
    "ax3.set_xlabel(\"rs1131017\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'rs1131017')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 3, figsize=(15, 5))\n",
    "ax1, ax2, ax3 = axes\n",
    "cd4ydata = (common_individuals_individual_ratio_dfv3['CD8T TEM']) / common_individuals_individual_ratio_dfv3['CD8T']\n",
    "sns.regplot(x=common_individuals_individual_ratio_dfv3['gt'],\n",
    "            y=cd4ydata, \n",
    "            ax=ax1)\n",
    "r, p = stats.spearmanr(common_individuals_individual_ratio_dfv3['gt'],\n",
    "                       cd4ydata)\n",
    "ax1.set_title('Oelen v3 r={:.2f}, p={:.2g}'.format(r, p))\n",
    "ax1.set_ylabel('CD8 TEM / CD4T')\n",
    "ax1.set_xlabel(\"rs1131017\")\n",
    "\n",
    "cd8tydata = (common_individuals_individual_ratio_dfv3['CD8T Naive']) / common_individuals_individual_ratio_dfv3['CD8T']\n",
    "sns.regplot(x=common_individuals_individual_ratio_dfv3['gt'],\n",
    "            y= cd8tydata, \n",
    "            ax=ax2)\n",
    "r, p = stats.spearmanr(common_individuals_individual_ratio_dfv3['gt'],\n",
    "                       cd8tydata)\n",
    "ax2.set_title('Oelen v3 r={:.2f}, p={:.2g}'.format(r, p))\n",
    "ax2.set_ylabel('CD8 Naive / CD8T')\n",
    "ax2.set_xlabel(\"rs1131017\")\n",
    "\n",
    "cd8tydata = (common_individuals_individual_ratio_dfv3['CD8T Naive']) / common_individuals_individual_ratio_dfv3['CD8T TEM']\n",
    "sns.regplot(x=common_individuals_individual_ratio_dfv3['gt'],\n",
    "            y= cd8tydata, \n",
    "            ax=ax3)\n",
    "r, p = stats.spearmanr(common_individuals_individual_ratio_dfv3['gt'],\n",
    "                       cd8tydata)\n",
    "ax3.set_title('Oelen v3 r={:.2f}, p={:.2g}'.format(r, p))\n",
    "ax3.set_ylabel('CD8 Naive / CD8T TEM')\n",
    "ax3.set_xlabel(\"rs1131017\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(104, 11) (72, 11) (32, 11)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CD4T TEM</th>\n",
       "      <th>CD4T Naive</th>\n",
       "      <th>CD8T TEM</th>\n",
       "      <th>CD8T Naive</th>\n",
       "      <th>CD4T</th>\n",
       "      <th>CD8T</th>\n",
       "      <th>CD4T TCM</th>\n",
       "      <th>CD8T TCM</th>\n",
       "      <th>all_num</th>\n",
       "      <th>gt</th>\n",
       "      <th>chemistry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>LLDeep_1035</th>\n",
       "      <td>17</td>\n",
       "      <td>152</td>\n",
       "      <td>36</td>\n",
       "      <td>54</td>\n",
       "      <td>625</td>\n",
       "      <td>108</td>\n",
       "      <td>424</td>\n",
       "      <td>28</td>\n",
       "      <td>1006</td>\n",
       "      <td>1.0</td>\n",
       "      <td>v2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LLDeep_0960</th>\n",
       "      <td>36</td>\n",
       "      <td>89</td>\n",
       "      <td>113</td>\n",
       "      <td>13</td>\n",
       "      <td>586</td>\n",
       "      <td>143</td>\n",
       "      <td>374</td>\n",
       "      <td>26</td>\n",
       "      <td>1546</td>\n",
       "      <td>0.0</td>\n",
       "      <td>v2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LLDeep_1004</th>\n",
       "      <td>65</td>\n",
       "      <td>54</td>\n",
       "      <td>440</td>\n",
       "      <td>9</td>\n",
       "      <td>583</td>\n",
       "      <td>462</td>\n",
       "      <td>437</td>\n",
       "      <td>16</td>\n",
       "      <td>1493</td>\n",
       "      <td>1.0</td>\n",
       "      <td>v2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LLDeep_0918</th>\n",
       "      <td>19</td>\n",
       "      <td>34</td>\n",
       "      <td>119</td>\n",
       "      <td>7</td>\n",
       "      <td>249</td>\n",
       "      <td>136</td>\n",
       "      <td>168</td>\n",
       "      <td>14</td>\n",
       "      <td>598</td>\n",
       "      <td>1.0</td>\n",
       "      <td>v2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LLDeep_0067</th>\n",
       "      <td>51</td>\n",
       "      <td>101</td>\n",
       "      <td>122</td>\n",
       "      <td>89</td>\n",
       "      <td>753</td>\n",
       "      <td>231</td>\n",
       "      <td>546</td>\n",
       "      <td>30</td>\n",
       "      <td>1429</td>\n",
       "      <td>1.0</td>\n",
       "      <td>v2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             CD4T TEM  CD4T Naive  CD8T TEM  CD8T Naive  CD4T  CD8T  CD4T TCM  \\\n",
       "LLDeep_1035        17         152        36          54   625   108       424   \n",
       "LLDeep_0960        36          89       113          13   586   143       374   \n",
       "LLDeep_1004        65          54       440           9   583   462       437   \n",
       "LLDeep_0918        19          34       119           7   249   136       168   \n",
       "LLDeep_0067        51         101       122          89   753   231       546   \n",
       "\n",
       "             CD8T TCM  all_num   gt chemistry  \n",
       "LLDeep_1035        28     1006  1.0        v2  \n",
       "LLDeep_0960        26     1546  0.0        v2  \n",
       "LLDeep_1004        16     1493  1.0        v2  \n",
       "LLDeep_0918        14      598  1.0        v2  \n",
       "LLDeep_0067        30     1429  1.0        v2  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "concate_v2_v3 = pd.concat([common_individuals_individual_ratio_df,\n",
    "                           common_individuals_individual_ratio_dfv3],\n",
    "                         axis=0)\n",
    "print(concate_v2_v3.shape,common_individuals_individual_ratio_df.shape, common_individuals_individual_ratio_dfv3.shape)\n",
    "concate_v2_v3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax1 = plt.subplots()\n",
    "cd4ydata = (concate_v2_v3['CD8T Naive'] + \\\n",
    "            concate_v2_v3['CD4T Naive']\n",
    "            ) / (\n",
    "    concate_v2_v3['CD8T TEM'] + \\\n",
    "            concate_v2_v3['CD4T TEM']\n",
    ")\n",
    "sns.regplot(x=concate_v2_v3['gt'],\n",
    "            y=cd4ydata, \n",
    "            ax=ax1)\n",
    "r, p = stats.spearmanr(concate_v2_v3['gt'],\n",
    "                       cd4ydata)\n",
    "ax1.set_title('Oelen v2 & v3 r={:.2f}, p={:.2g}'.format(r, p))\n",
    "ax1.set_ylabel('CD8+CD4 TEM / CD8+CD4 Naive')\n",
    "ax1.set_xlabel(\"rs1131017\")\n",
    "\n",
    "plt.savefig('TEM_naive_CD4_CD8_v2_v3_rs1131017.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
